{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=6 font-weight=bold face=\"微软雅黑\">Capital Bikeshare自行车数据回归分析--Day.csv</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=5 face=\"微软雅黑\">1.1 任务描述</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。训练数据为2011年的数据，要求预测2012年每天的单车共享数量。\n",
    "\n",
    "1. 对数据做数据探索分析（可参考EDA_BikeSharing.ipynb，不计分） \n",
    "2. 适当的特征工程（可参考FE_BikeSharing.ipynb，不计分） \n",
    "3. 对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，评价指标为RMSE。（10分） \n",
    "4. 用训练数据训练最小二乘线性回归模型（20分）、岭回归模型、Lasso模型，其中岭回归模型（30分）和Lasso模型（30分），注意岭回归模型和Lasso模型的正则超参数调优。 \n",
    "5. 比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能。并简单说明原因。（10分） \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=5 face=\"微软雅黑\">1.1.1 准备工作，导入工具包</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=gray size=5 face=\"微软雅黑\">1.2 数据读取</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day_list : (731, 35)\n"
     ]
    }
   ],
   "source": [
    "# 读入训练数据和测试数据\n",
    "day_list = pd.read_csv(\"FE_day.csv\")\n",
    "print(\"day_list : \" + str(day_list.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 搭建模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 从训练集中分离一部分作为测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从原始数据中分离输入特征X和输出y\n",
    "y_d_train = day_list['cnt'].values\n",
    "X_d_train = day_list.drop('cnt', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据分割训练数据和测试数据\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "#随机采样25%的数据构建测试样本，其余作为训练样本\n",
    "X_day_train, X_day_test, y_day_train, y_day_test = train_test_split(X_d_train, y_d_train, random_state = 33, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\python3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\ProgramData\\Anaconda3\\envs\\python3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "X_day_train.drop(['instant'],axis=1, inplace = True)\n",
    "testID = X_day_test['instant']\n",
    "X_day_test.drop(['instant'],axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.734175</td>\n",
       "      <td>0.705849</td>\n",
       "      <td>0.670951</td>\n",
       "      <td>0.239759</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773634</td>\n",
       "      <td>0.719927</td>\n",
       "      <td>0.768209</td>\n",
       "      <td>0.243590</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.201489</td>\n",
       "      <td>0.156435</td>\n",
       "      <td>0.553128</td>\n",
       "      <td>0.700017</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.682257</td>\n",
       "      <td>0.649499</td>\n",
       "      <td>0.522280</td>\n",
       "      <td>0.417954</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.718600</td>\n",
       "      <td>0.678498</td>\n",
       "      <td>0.754928</td>\n",
       "      <td>0.221797</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.523386</td>\n",
       "      <td>0.518553</td>\n",
       "      <td>0.606684</td>\n",
       "      <td>0.424379</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.321941</td>\n",
       "      <td>0.340356</td>\n",
       "      <td>0.682519</td>\n",
       "      <td>0.079507</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.819323</td>\n",
       "      <td>0.773805</td>\n",
       "      <td>0.701799</td>\n",
       "      <td>0.425641</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.490158</td>\n",
       "      <td>0.494504</td>\n",
       "      <td>0.789203</td>\n",
       "      <td>0.124372</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.776749</td>\n",
       "      <td>0.732364</td>\n",
       "      <td>0.676949</td>\n",
       "      <td>0.175637</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.329209</td>\n",
       "      <td>0.310549</td>\n",
       "      <td>0.298201</td>\n",
       "      <td>0.339744</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.510926</td>\n",
       "      <td>0.508605</td>\n",
       "      <td>0.522280</td>\n",
       "      <td>0.343610</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.668758</td>\n",
       "      <td>0.618044</td>\n",
       "      <td>0.889889</td>\n",
       "      <td>0.393568</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.303250</td>\n",
       "      <td>0.289006</td>\n",
       "      <td>0.557841</td>\n",
       "      <td>0.346160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.765327</td>\n",
       "      <td>0.719101</td>\n",
       "      <td>0.716795</td>\n",
       "      <td>0.298736</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.677065</td>\n",
       "      <td>0.651150</td>\n",
       "      <td>0.507283</td>\n",
       "      <td>0.333343</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.863973</td>\n",
       "      <td>0.824359</td>\n",
       "      <td>0.678234</td>\n",
       "      <td>0.220508</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.471467</td>\n",
       "      <td>0.464675</td>\n",
       "      <td>0.554413</td>\n",
       "      <td>0.438493</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.131919</td>\n",
       "      <td>0.109191</td>\n",
       "      <td>0.550985</td>\n",
       "      <td>0.503869</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.771557</td>\n",
       "      <td>0.707512</td>\n",
       "      <td>0.620394</td>\n",
       "      <td>0.320521</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.457969</td>\n",
       "      <td>0.456360</td>\n",
       "      <td>0.881748</td>\n",
       "      <td>0.256406</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.784017</td>\n",
       "      <td>0.755582</td>\n",
       "      <td>0.709512</td>\n",
       "      <td>0.258983</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.246140</td>\n",
       "      <td>0.245924</td>\n",
       "      <td>0.743359</td>\n",
       "      <td>0.229511</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.201489</td>\n",
       "      <td>0.228509</td>\n",
       "      <td>0.503856</td>\n",
       "      <td>0.091018</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.257562</td>\n",
       "      <td>0.234307</td>\n",
       "      <td>0.405313</td>\n",
       "      <td>0.385880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.740406</td>\n",
       "      <td>0.681002</td>\n",
       "      <td>0.856898</td>\n",
       "      <td>0.396136</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.809978</td>\n",
       "      <td>0.769676</td>\n",
       "      <td>0.763067</td>\n",
       "      <td>0.379481</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.794402</td>\n",
       "      <td>0.756418</td>\n",
       "      <td>0.833334</td>\n",
       "      <td>0.247447</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.806862</td>\n",
       "      <td>0.754722</td>\n",
       "      <td>0.637103</td>\n",
       "      <td>0.303830</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.658374</td>\n",
       "      <td>0.629607</td>\n",
       "      <td>0.654242</td>\n",
       "      <td>0.103876</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.412280</td>\n",
       "      <td>0.396741</td>\n",
       "      <td>0.428449</td>\n",
       "      <td>0.493588</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.840090</td>\n",
       "      <td>0.806955</td>\n",
       "      <td>0.727506</td>\n",
       "      <td>0.308966</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.795440</td>\n",
       "      <td>0.737336</td>\n",
       "      <td>0.370180</td>\n",
       "      <td>0.514117</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.965734</td>\n",
       "      <td>0.928746</td>\n",
       "      <td>0.502571</td>\n",
       "      <td>0.294854</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.316749</td>\n",
       "      <td>0.302269</td>\n",
       "      <td>0.541560</td>\n",
       "      <td>0.321817</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.173453</td>\n",
       "      <td>0.158912</td>\n",
       "      <td>0.327335</td>\n",
       "      <td>0.419242</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.440316</td>\n",
       "      <td>0.443951</td>\n",
       "      <td>0.842331</td>\n",
       "      <td>0.470498</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.752866</td>\n",
       "      <td>0.715782</td>\n",
       "      <td>0.607969</td>\n",
       "      <td>0.330758</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.354311</td>\n",
       "      <td>0.357088</td>\n",
       "      <td>0.699229</td>\n",
       "      <td>0.212044</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.941851</td>\n",
       "      <td>0.980934</td>\n",
       "      <td>0.710797</td>\n",
       "      <td>0.411546</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.529616</td>\n",
       "      <td>0.516879</td>\n",
       "      <td>0.589118</td>\n",
       "      <td>0.196147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.820361</td>\n",
       "      <td>0.772142</td>\n",
       "      <td>0.696658</td>\n",
       "      <td>0.244886</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.241986</td>\n",
       "      <td>0.214393</td>\n",
       "      <td>0.774208</td>\n",
       "      <td>0.210260</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.455891</td>\n",
       "      <td>0.441457</td>\n",
       "      <td>0.762211</td>\n",
       "      <td>0.660264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.147494</td>\n",
       "      <td>0.103388</td>\n",
       "      <td>0.470008</td>\n",
       "      <td>0.682065</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>584 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "630         0         0         1         0       0       0       0       0   \n",
       "192         0         0         1         0       0       0       0       0   \n",
       "275         0         0         0         1       0       0       0       0   \n",
       "367         1         0         0         0       1       0       0       0   \n",
       "296         0         0         0         1       0       0       0       0   \n",
       "633         0         0         0         1       0       0       0       0   \n",
       "705         0         0         0         1       0       0       0       0   \n",
       "400         1         0         0         0       0       1       0       0   \n",
       "175         0         0         1         0       0       0       0       0   \n",
       "325         0         0         0         1       0       0       0       0   \n",
       "322         0         0         0         1       0       0       0       0   \n",
       "142         0         1         0         0       0       0       0       0   \n",
       "682         0         0         0         1       0       0       0       0   \n",
       "645         0         0         0         1       0       0       0       0   \n",
       "42          1         0         0         0       0       1       0       0   \n",
       "155         0         1         0         0       0       0       0       0   \n",
       "510         0         1         0         0       0       0       0       0   \n",
       "38          1         0         0         0       0       1       0       0   \n",
       "470         0         1         0         0       0       0       0       1   \n",
       "597         0         0         1         0       0       0       0       0   \n",
       "125         0         1         0         0       0       0       0       0   \n",
       "347         0         0         0         1       0       0       0       0   \n",
       "183         0         0         1         0       0       0       0       0   \n",
       "702         0         0         0         1       0       0       0       0   \n",
       "177         0         0         1         0       0       0       0       0   \n",
       "451         0         1         0         0       0       0       1       0   \n",
       "396         1         0         0         0       0       1       0       0   \n",
       "567         0         0         1         0       0       0       0       0   \n",
       "357         1         0         0         0       0       0       0       0   \n",
       "256         0         0         1         0       0       0       0       0   \n",
       "..        ...       ...       ...       ...     ...     ...     ...     ...   \n",
       "519         0         1         0         0       0       0       0       0   \n",
       "579         0         0         1         0       0       0       0       0   \n",
       "650         0         0         0         1       0       0       0       0   \n",
       "7           1         0         0         0       1       0       0       0   \n",
       "595         0         0         1         0       0       0       0       0   \n",
       "99          0         1         0         0       0       0       0       1   \n",
       "535         0         1         0         0       0       0       0       0   \n",
       "403         1         0         0         0       0       1       0       0   \n",
       "344         0         0         0         1       0       0       0       0   \n",
       "84          0         1         0         0       0       0       1       0   \n",
       "528         0         1         0         0       0       0       0       0   \n",
       "246         0         0         1         0       0       0       0       0   \n",
       "614         0         0         1         0       0       0       0       0   \n",
       "592         0         0         1         0       0       0       0       0   \n",
       "662         0         0         0         1       0       0       0       0   \n",
       "395         1         0         0         0       1       0       0       0   \n",
       "172         0         0         1         0       0       0       0       0   \n",
       "543         0         0         1         0       0       0       0       0   \n",
       "545         0         0         1         0       0       0       0       0   \n",
       "398         1         0         0         0       0       1       0       0   \n",
       "61          1         0         0         0       0       0       1       0   \n",
       "102         0         1         0         0       0       0       0       1   \n",
       "195         0         0         1         0       0       0       0       0   \n",
       "57          1         0         0         0       0       1       0       0   \n",
       "201         0         0         1         0       0       0       0       0   \n",
       "658         0         0         0         1       0       0       0       0   \n",
       "578         0         0         1         0       0       0       0       0   \n",
       "728         1         0         0         0       0       0       0       0   \n",
       "391         1         0         0         0       1       0       0       0   \n",
       "20          1         0         0         0       1       0       0       0   \n",
       "\n",
       "     mnth_5  mnth_6 ...  weekday_4  weekday_5  weekday_6      temp     atemp  \\\n",
       "630       0       0 ...          0          0          1  0.736253  0.697558   \n",
       "192       0       0 ...          0          0          0  0.915892  0.866609   \n",
       "275       0       0 ...          0          0          0  0.405012  0.410824   \n",
       "367       0       0 ...          0          0          0  0.113228  0.061963   \n",
       "296       0       0 ...          0          0          0  0.503656  0.496173   \n",
       "633       0       0 ...          0          0          0  0.611648  0.610519   \n",
       "705       0       0 ...          1          0          0  0.245101  0.235138   \n",
       "400       0       0 ...          0          0          0  0.257562  0.243430   \n",
       "175       0       1 ...          0          0          1  0.792325  0.740646   \n",
       "325       0       0 ...          0          0          0  0.445508  0.449743   \n",
       "322       0       0 ...          0          0          1  0.336479  0.322138   \n",
       "142       1       0 ...          0          0          0  0.713409  0.671054   \n",
       "682       0       0 ...          0          0          0  0.354130  0.320487   \n",
       "645       0       0 ...          0          0          0  0.444469  0.447272   \n",
       "42        0       0 ...          0          0          1  0.203567  0.201994   \n",
       "155       0       1 ...          0          0          0  0.734175  0.705849   \n",
       "510       1       0 ...          0          1          0  0.773634  0.719927   \n",
       "38        0       0 ...          0          0          0  0.201489  0.156435   \n",
       "470       0       0 ...          0          0          0  0.682257  0.649499   \n",
       "597       0       0 ...          0          0          0  0.718600  0.678498   \n",
       "125       1       0 ...          0          1          0  0.523386  0.518553   \n",
       "347       0       0 ...          0          0          0  0.321941  0.340356   \n",
       "183       0       0 ...          0          0          0  0.819323  0.773805   \n",
       "702       0       0 ...          0          0          0  0.490158  0.494504   \n",
       "177       0       1 ...          0          0          0  0.776749  0.732364   \n",
       "451       0       0 ...          0          0          0  0.329209  0.310549   \n",
       "396       0       0 ...          0          0          0  0.510926  0.508605   \n",
       "567       0       0 ...          0          0          1  0.668758  0.618044   \n",
       "357       0       0 ...          0          0          1  0.303250  0.289006   \n",
       "256       0       0 ...          0          0          0  0.765327  0.719101   \n",
       "..      ...     ... ...        ...        ...        ...       ...       ...   \n",
       "519       0       1 ...          0          0          0  0.677065  0.651150   \n",
       "579       0       0 ...          1          0          0  0.863973  0.824359   \n",
       "650       0       0 ...          0          1          0  0.471467  0.464675   \n",
       "7         0       0 ...          0          0          1  0.131919  0.109191   \n",
       "595       0       0 ...          0          0          1  0.771557  0.707512   \n",
       "99        0       0 ...          0          0          0  0.457969  0.456360   \n",
       "535       0       1 ...          0          0          0  0.784017  0.755582   \n",
       "403       0       0 ...          0          0          0  0.246140  0.245924   \n",
       "344       0       0 ...          0          0          0  0.201489  0.228509   \n",
       "84        0       0 ...          0          0          1  0.257562  0.234307   \n",
       "528       0       1 ...          0          0          0  0.740406  0.681002   \n",
       "246       0       0 ...          0          0          0  0.809978  0.769676   \n",
       "614       0       0 ...          1          0          0  0.794402  0.756418   \n",
       "592       0       0 ...          0          0          0  0.806862  0.754722   \n",
       "662       0       0 ...          0          0          0  0.658374  0.629607   \n",
       "395       0       0 ...          0          0          0  0.412280  0.396741   \n",
       "172       0       1 ...          0          0          0  0.840090  0.806955   \n",
       "543       0       1 ...          0          0          0  0.795440  0.737336   \n",
       "545       0       1 ...          0          1          0  0.965734  0.928746   \n",
       "398       0       0 ...          0          1          0  0.316749  0.302269   \n",
       "61        0       0 ...          1          0          0  0.173453  0.158912   \n",
       "102       0       0 ...          0          0          0  0.440316  0.443951   \n",
       "195       0       0 ...          0          1          0  0.752866  0.715782   \n",
       "57        0       0 ...          0          0          0  0.354311  0.357088   \n",
       "201       0       0 ...          1          0          0  0.941851  0.980934   \n",
       "658       0       0 ...          0          0          1  0.529616  0.516879   \n",
       "578       0       0 ...          0          0          0  0.820361  0.772142   \n",
       "728       0       0 ...          0          0          1  0.241986  0.214393   \n",
       "391       0       0 ...          0          1          0  0.455891  0.441457   \n",
       "20        0       0 ...          0          1          0  0.147494  0.103388   \n",
       "\n",
       "          hum  windspeed  holiday  workingday  yr  \n",
       "630  0.664953   0.538460        0           0   1  \n",
       "192  0.574979   0.366672        0           1   0  \n",
       "275  0.782348   0.125660        0           1   0  \n",
       "367  0.453728   0.707688        0           1   1  \n",
       "296  0.793916   0.198734        0           1   0  \n",
       "633  0.586118   0.441027        0           1   1  \n",
       "705  0.523136   0.314103        0           1   1  \n",
       "400  0.707370   0.316663        0           0   1  \n",
       "175  0.497001   0.385880        0           0   0  \n",
       "325  0.989717   0.198734        0           1   0  \n",
       "322  0.516281   0.416649        0           0   0  \n",
       "142  0.832905   0.435916        0           1   0  \n",
       "682  0.681663   0.658984        0           1   1  \n",
       "645  0.728363   0.244851        0           0   1  \n",
       "42   0.559555   0.373090        0           0   0  \n",
       "155  0.670951   0.239759        0           0   0  \n",
       "510  0.768209   0.243590        0           1   1  \n",
       "38   0.553128   0.700017        0           1   0  \n",
       "470  0.522280   0.417954        0           0   1  \n",
       "597  0.754928   0.221797        0           1   1  \n",
       "125  0.606684   0.424379        0           1   0  \n",
       "347  0.682519   0.079507        0           1   0  \n",
       "183  0.701799   0.425641        0           0   0  \n",
       "702  0.789203   0.124372        0           1   1  \n",
       "177  0.676949   0.175637        0           1   0  \n",
       "451  0.298201   0.339744        0           1   1  \n",
       "396  0.522280   0.343610        0           1   1  \n",
       "567  0.889889   0.393568        0           0   1  \n",
       "357  0.557841   0.346160        0           0   0  \n",
       "256  0.716795   0.298736        0           1   0  \n",
       "..        ...        ...      ...         ...  ..  \n",
       "519  0.507283   0.333343        0           0   1  \n",
       "579  0.678234   0.220508        0           1   1  \n",
       "650  0.554413   0.438493        0           1   1  \n",
       "7    0.550985   0.503869        0           0   0  \n",
       "595  0.620394   0.320521        0           0   1  \n",
       "99   0.881748   0.256406        0           0   0  \n",
       "535  0.709512   0.258983        0           1   1  \n",
       "403  0.743359   0.229511        0           1   1  \n",
       "344  0.503856   0.091018        0           0   0  \n",
       "84   0.405313   0.385880        0           0   0  \n",
       "528  0.856898   0.396136        0           1   1  \n",
       "246  0.763067   0.379481        0           0   0  \n",
       "614  0.833334   0.247447        0           1   1  \n",
       "592  0.637103   0.303830        0           1   1  \n",
       "662  0.654242   0.103876        0           1   1  \n",
       "395  0.428449   0.493588        0           1   1  \n",
       "172  0.727506   0.308966        0           1   0  \n",
       "543  0.370180   0.514117        0           1   1  \n",
       "545  0.502571   0.294854        0           1   1  \n",
       "398  0.541560   0.321817        0           1   1  \n",
       "61   0.327335   0.419242        0           1   0  \n",
       "102  0.842331   0.470498        0           1   0  \n",
       "195  0.607969   0.330758        0           1   0  \n",
       "57   0.699229   0.212044        0           0   0  \n",
       "201  0.710797   0.411546        0           1   0  \n",
       "658  0.589118   0.196147        0           0   1  \n",
       "578  0.696658   0.244886        0           1   1  \n",
       "728  0.774208   0.210260        0           0   1  \n",
       "391  0.762211   0.660264        0           1   1  \n",
       "20   0.470008   0.682065        0           1   0  \n",
       "\n",
       "[584 rows x 33 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_day_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean y diff : -0.0480472514231\n"
     ]
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化对目标值的标准化器\n",
    "# 对y标准化不是必须，但对其进行标准化可以使得不同问题w的取值范围相对相同\n",
    "y_train = y_day_train\n",
    "X_train = X_day_train\n",
    "\n",
    "y_test = y_day_test\n",
    "X_test = X_day_test\n",
    "# 求出y的均值及标准差\n",
    "mean_y = y_train.mean()\n",
    "std_y = y_train.std()\n",
    "\n",
    "y_train = (y_train - mean_y)/std_y\n",
    "\n",
    "y_test = (y_test - mean_y)/std_y\n",
    "\n",
    "mean_test_y = y_test.mean()\n",
    "#mean_train_y = 0\n",
    "mean_diff = mean_test_y;\n",
    "\n",
    "print(\"mean y diff :\", mean_diff)\n",
    "\n",
    "#y_day_train = ss_y.fit_transform(y_day_train.reshape(-1, 1))\n",
    "#y_day_test = ss_y.transform(y_day_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 模型测试（三种）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 OLS缺省参数的线性回归模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对day数据进行OLS缺省参数的线性回归  \n",
    "\n",
    "最小二乘线性回归  \n",
    "\n",
    "最小二乘没有超参数需要调优，直接用全体训练数据训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#模型评估\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -4.03888524e-01,   3.53350781e-02,  -7.43153481e-02,\n",
       "         4.48888830e-01,  -4.51438601e+12,  -4.51438601e+12,\n",
       "        -4.51438601e+12,  -4.51438601e+12,  -4.51438601e+12,\n",
       "        -4.51438601e+12,  -4.51438601e+12,  -4.51438601e+12,\n",
       "        -4.51438601e+12,  -4.51438601e+12,  -4.51438601e+12,\n",
       "        -4.51438601e+12,   3.57987563e+13,   3.57987563e+13,\n",
       "         3.57987563e+13,  -2.82249398e+13,  -1.33374354e+13,\n",
       "        -1.33374354e+13,  -1.33374354e+13,  -1.33374354e+13,\n",
       "        -1.33374354e+13,  -2.82249398e+13,   1.29333496e+00,\n",
       "         5.76049805e-01,  -7.20214844e-01,  -5.93353271e-01,\n",
       "        -1.48875043e+13,  -1.48875043e+13,   1.04907227e+00])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#线性回归\n",
    "#class sklearn,linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "#使用没人配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "#训练模型参数\n",
    "model = lr.fit(X_train, y_train)\n",
    "\n",
    "#预测，下面计算score会自动调用predict\n",
    "lr_y_predict_test = model.predict(X_test)\n",
    "lr_y_predict_test += mean_diff\n",
    "lr_y_predict_train = model.predict(X_train)\n",
    "\n",
    "#显示特征的回归系数\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on Training set : 0.386906131579\n",
      "RMSE on Test set : 0.428284936572\n"
     ]
    }
   ],
   "source": [
    "rmse_train = np.sqrt(mean_squared_error(y_train,lr_y_predict_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test,lr_y_predict_test))\n",
    "print(\"RMSE on Training set :\", rmse_train)\n",
    "print(\"RMSE on Test set :\", rmse_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.823083412093\n",
      "The r2 score of LinearRegression on train is 0.850303645347\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is', r2_score(y_test, lr_y_predict_test))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, lr_y_predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\python3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28c40e35ba8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 5))\n",
    "f.tight_layout()\n",
    "ax.hist(y_train - lr_y_predict_train, bins = 40, label = 'Residuals Linear', color = \"g\", normed = True)\n",
    "ax.set_title(\"Histogram of Residuals\")\n",
    "ax.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察预测值与真值的散点图\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_train, lr_y_predict_train)\n",
    "#数据已经标准化，3倍残差即可\n",
    "plt.plot([-3, 3], [-3, 3], '--k')\n",
    "plt.axis('tight')\n",
    "plt.xlabel(\"True cnt\")\n",
    "plt.ylabel(\"Predicted cnt\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 岭回归模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对day数据进行岭回归 Linear Regression with Ridge regularization (L2 penalty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha : 1.0\n"
     ]
    }
   ],
   "source": [
    "#RidgeCV缺省的score是mean squared errors \n",
    "# 1. 生成学习器实例\n",
    "# RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None, store_cv_values=False)\n",
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "day_alphas = [0.01, 0.1, 1, 10, 100, 1000]\n",
    "day_reg = RidgeCV(alphas=day_alphas, store_cv_values=True)\n",
    "# 2. 用训练数据度模型进行训练\n",
    "# RidgeCV采用的是广义交叉验证（Generalized Cross-Validation），留一交叉验证（N-折交叉验证）的一种有效实现方式\n",
    "day_reg.fit(X_train, y_train)\n",
    "\n",
    "#通过交叉验证得到的最佳超参数alpha\n",
    "best_alpha = day_reg.alpha_\n",
    "print(\"Best alpha :\", best_alpha)\n",
    "\n",
    "#预测\n",
    "day_reg_y_predict_test = day_reg.predict(X_test)\n",
    "day_reg_y_predict_test += mean_diff\n",
    "day_reg_y_predict_train = day_reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is:  1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.41624277,  0.05165424, -0.04281393,  0.40740246, -0.21065855,\n",
       "       -0.16279212,  0.08589728,  0.00615118,  0.17297197,  0.18157539,\n",
       "       -0.12130579,  0.13320008,  0.39912313,  0.11128457, -0.28530799,\n",
       "       -0.31013918,  0.47500146,  0.20073664, -0.67573811, -0.09978889,\n",
       "       -0.10649253, -0.01725286,  0.03157369,  0.03142763,  0.0428992 ,\n",
       "        0.11763376,  0.92880767,  0.80199096, -0.58631739, -0.56128312,\n",
       "       -0.12758952,  0.10974465,  1.047439  ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_mse_mean = np.mean(day_reg.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(day_alphas), day_mse_mean.reshape(len(day_alphas), 1))\n",
    "plt.plot(np.log10(best_alpha)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('day_mse')\n",
    "plt.show()\n",
    "\n",
    "print('alpha is: ', best_alpha)\n",
    "day_reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on Training set : 0.386999777829\n",
      "RMSE on Test set : 0.426688848929\n"
     ]
    }
   ],
   "source": [
    "#训练上测试，训练误差，实际任务中这一步不需要\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train,day_reg_y_predict_train))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test,day_reg_y_predict_test))\n",
    "\n",
    "print(\"RMSE on Training set :\", rmse_train)\n",
    "print(\"RMSE on Test set :\", rmse_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.824399583613\n",
      "The r2 score of RidgeCV on train is 0.85023117196\n"
     ]
    }
   ],
   "source": [
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, day_reg_y_predict_test))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, day_reg_y_predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge picked 33 features and eliminated the other 0 features\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot important coefficients\n",
    "coefs = pd.Series(day_reg.coef_, index = X_train.columns)\n",
    "print(\"Ridge picked \" + str(sum(coefs != 0)) + \" features and eliminated the other \" +  \\\n",
    "      str(sum(coefs == 0)) + \" features\")\n",
    "\n",
    "#正系数值最大的10个特征和负系数值最小（绝对值大）的10个特征\n",
    "imp_coefs = pd.concat([coefs.sort_values().head(10),\n",
    "                     coefs.sort_values().tail(10)])\n",
    "imp_coefs.plot(kind = \"barh\")\n",
    "plt.title(\"Coefficients in the Ridge Model\")\n",
    "plt.show()\n",
    "\n",
    "mse_mean = np.mean(day_reg.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(day_alphas), mse_mean.reshape(len(day_alphas),1)) \n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第一张图中, 与骑行量正相关比较强的是年份temp气温, weathersit好天气, month月份5,6,8,9月, 负相关的特征则会导致骑  \n",
    "### 行量的下降, 如风速, 大雨大雾天,湿度等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第二张图中, 参数的曲线较为曲折"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.3 Lasso模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对day数据做lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha : 0.0019825214825\n"
     ]
    }
   ],
   "source": [
    "###Lasso/ L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import  LassoCV\n",
    "\n",
    "lasso = LassoCV()\n",
    "\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "alpha = lasso.alpha_\n",
    "print(\"Best alpha :\", alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv of rmse : 0.425746187682\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()   \n",
    "\n",
    "mse_cv = np.mean(lasso.mse_path_, axis = 1)\n",
    "rmse_cv = np.sqrt(mse_cv)\n",
    "print(\"cv of rmse :\", min(rmse_cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso picked 28 features and eliminated the other 5 features\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot important coefficients\n",
    "coefs = pd.Series(lasso.coef_, index = X_train.columns)\n",
    "print(\"Lasso picked \" + str(sum(coefs != 0)) + \" features and eliminated the other \" +  \\\n",
    "      str(sum(coefs == 0)) + \" features\")\n",
    "imp_coefs = pd.concat([coefs.sort_values().head(10),\n",
    "                     coefs.sort_values().tail(10)])\n",
    "imp_coefs.plot(kind = \"barh\")\n",
    "plt.title(\"Coefficients in the Lasso Model\")\n",
    "plt.show()\n",
    "\n",
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图一中, 与骑行量正相关比较强的是temp气温, weathersit好天气, month月份3,6,5,10月, 负相关的特征则会导致骑  \n",
    "### 行量的下降, 如风速, 大雨大雾天,湿度等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第二张图中, 参数的曲线较为平滑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#预测\n",
    "lasso_y_day_predict_test = lasso.predict(X_test)\n",
    "lasso_y_day_predict_test += mean_diff\n",
    "lasso_y_predict_day_train = lasso.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on Training set : 0.389095279536\n",
      "RMSE on Test set : 0.42197848637\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = lasso_y_day_predict_test\n",
    "y_train_pred = lasso_y_predict_day_train\n",
    "\n",
    "rmse_train = np.sqrt(mean_squared_error(y_train,y_train_pred))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_test,y_test_pred))\n",
    "print(\"RMSE on Training set :\", rmse_train)\n",
    "print(\"RMSE on Test set :\", rmse_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.828255208529\n",
      "The r2 score of LassoCV on train is 0.848604863443\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_test, lasso_y_day_predict_test))\n",
    "#训练集\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, lasso_y_predict_day_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三种回归模型的比较"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLS 损失函数（平方损失）\n",
    "最小二乘（OLS）线性回归中，目标函数只考虑了模型对训练样本的拟合程度"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge 损失函数（平方损失）+正则化（L2范数）\n",
    "![image.png](attachment:image.png)\n",
    "Ridge 回归通过对系数的大小施加惩罚来解决 普通最小二乘法 的一些问题。 岭系数最小化的是带罚项的残差平方和，其中, α≥0\n",
    "α是控制系数收缩量的复杂性参数, α的值越大，收缩量越大，这样系数对共线性的鲁棒性也更强。  \n",
    "\n",
    "L2正则使得线性回归系数收缩，模型稳定。  \n",
    "\n",
    "适用场景: 当输入特征之间存在共线性时使用L2正则, 即当特征之间相关度较高的时候."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso 损失函数（平方损失）+正则化（L1范数）\n",
    "![image.png](attachment:image.png)  \n",
    "L1正则可以解决过拟合问题, 也会收缩回归系数。当正则参数取合适值时，L1正则使得有些线性回归系数为0，得到稀疏模型, 即特征值减少了, 因此起到了特征选择的作用.\n",
    "\n",
    "适用场景:\n",
    "当输入特征多，有些特征与目标变量之间相关性很弱时， L1正则可能只选择强相关的特征，模型解释性好。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 综上所述  \n",
    "L1和L2正则都是比较常见和常用的正则化项，都可以达到防止过拟合的效果。L1正则化的解具有稀疏性，可用于特征选择。L2正则化的解都比较小，抗扰动能力强"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对测试集进行测试，生成提交文件\n",
    "#### 由于本次模型训练, 三种模型训练结果相似, 最后预测选其一即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = lasso.predict(X_test)\n",
    "y_test_pred += mean_diff\n",
    "y_test_pred = y_test_pred * std_y +  mean_y\n",
    "\n",
    "#生成提交测试结果\n",
    "\n",
    "df = pd.DataFrame({\"instant\":testID, 'cnt':y_test_pred})\n",
    "#df.reindex(columns=['instant'])\n",
    "#y = pd.Series(data = y_test_pred, name = 'cnt')\n",
    "#df = pd.concat([testID, y], axis = 1, ignore_index=True)\n",
    "df.to_csv('submission_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             cnt  instant\n",
      "607  7030.642704      608\n",
      "266  4940.536717      267\n",
      "495  6006.525495      496\n",
      "413  4174.058254      414\n",
      "374  3813.358766      375\n",
      "184  3488.695078      185\n",
      "156  4795.037039      157\n",
      "299  3855.885951      300\n",
      "710  4649.063213      711\n",
      "121  3505.305333      122\n",
      "149  4618.048669      150\n",
      "555  5540.585077      556\n",
      "600  6913.974134      601\n",
      "691  5403.665622      692\n",
      "305  3706.915749      306\n",
      "22    568.626429       23\n",
      "360   973.948980      361\n",
      "196  4182.115562      197\n",
      "471  5939.283650      472\n",
      "536  7526.654292      537\n",
      "415  3274.164249      416\n",
      "456  4668.325820      457\n",
      "31    947.467298       32\n",
      "449  4609.131932      450\n",
      "64    954.046034       65\n",
      "363  1763.290976      364\n",
      "366  3063.418843      367\n",
      "167  4730.987204      168\n",
      "8     490.436286        9\n",
      "286  4270.350432      287\n",
      "..           ...      ...\n",
      "615  7270.775817      616\n",
      "469  6015.923090      470\n",
      "76   3316.366663       77\n",
      "110  3707.340070      111\n",
      "320  2655.915097      321\n",
      "684  4880.070742      685\n",
      "181  4799.076268      182\n",
      "541  6622.965192      542\n",
      "586  7052.718700      587\n",
      "412  4089.535893      413\n",
      "678  5555.717847      679\n",
      "364  1978.196747      365\n",
      "260  4011.398258      261\n",
      "663  6390.642377      664\n",
      "382  3529.217933      383\n",
      "674  5119.842381      675\n",
      "187  4533.476368      188\n",
      "241  4615.300532      242\n",
      "635  7263.446506      636\n",
      "271  5543.641009      272\n",
      "249  2534.782866      250\n",
      "302  3564.477185      303\n",
      "685  5598.066589      686\n",
      "67   1436.715228       68\n",
      "219  4796.884098      220\n",
      "85   2236.079453       86\n",
      "422  3951.262278      423\n",
      "381  3134.854600      382\n",
      "37   1761.686525       38\n",
      "234  4684.244614      235\n",
      "\n",
      "[147 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print (df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
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 },
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 "nbformat_minor": 2
}
